Sample description

Institutions per country

universities_per_country <- works %>% 
  distinct(country_code, institution_id) %>% 
  count(country_code, name = "n_universities") %>% 
  arrange(desc(n_universities)) %>% 
  collect() %>% 
  add_country()

universities_per_country %>% 
  select(country, n_universities) %>% 
  knitr::kable()
<<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD
country n_universities
United States 6808
India 2061
China 1985
United Kingdom 1610
Japan 1444
Germany 1033
France 1024
Brazil 807
Canada 746
Russian Federation 665
Spain 642
Italy 582Brazil 31
Australia 538
Korea, Rep. 478Turkey 31
NA 433
Indonesia 407
Poland 333
Netherlands 329
Switzerland 293
Taiwan, China 264
Pakistan 240
Turkiye 240
Mexico 236
Iran, Islamic Rep. 234
Norway 219
Colombia 205Belgium 8
Israel 8
Switzerland 8
Egypt 8
Ukraine 199
Czech Republic195
Sweden 186
Portugal 181
Austria 177
Nigeria 171
South Africa 168
Belgium 158
Thailand 1567
Finland 7
New Zealand 7
Mexico 6
Thailand 6
Norway 6
Hungary 6
Malaysia 6
Ireland 6
Portugal 6
Argentina 151
Greece 145
Finland 144
Denmark 142
Ireland 138
Bangladesh 132
Philippines 122
Malaysia 121
Israel 120
Hungary 112Singapore 3
Chile 3
Tunisia 3
Romania 3
Argentina 3
Colombia 3
Serbia 3
New Zealand 108
Vietnam 103Slovenia 2
Chile 97
Singapore 97
Egypt, Arab Rep. 96
Saudi Arabia 91
Slovak Republic 89
Kenya 86
Romania 80
Bulgaria 80
Peru 78
Slovenia 76
Ecuador 65
Uganda 64
United Arab Emirates 60
Serbia 60
Croatia 59
Ethiopia 54
Ghana 54
Iraq 52
Kazakhstan 51
Tanzania 48Slovakia 2
Nigeria 2
Ghana 1
Lithuania 1
Uruguay 1
Algeria 1
Cyprus 1
Iceland 1
Oman 1
Kuwait 1
Uganda 1
Qatar 1
Ethiopia 1
Viet Nam 1
Luxembourg 1
Croatia 1
Estonia 1
Morocco 1
Lebanon 1
Algeria 47
Nepal 46
Belarus 40
Venezuela, RB 39
Latvia 38
Cuba 38
Sri Lanka 38
Lithuania 36
Estonia 33
Tunisia 33
Jordan 32
Zimbabwe 28
Morocco 28
Uzbekistan 27
Cameroon 27
Uruguay 27
Sudan 27
Lebanon 26
Cyprus 26
Costa Rica 25
Armenia 23
Luxembourg 23
Georgia 22
Oman 21
Albania 20
Cambodia 20
Bolivia 20
Azerbaijan 19
Bosnia and Herzegovina 19
Syrian Arab Republic 19
Myanmar 19
Kuwait 19
Qatar 19
Iceland 19
Congo, Dem. Rep. 18
West Bank and Gaza 17
Paraguay 17
Zambia 16
Panama 15
Malawi 15
Dominican Republic 15
Mongolia 14
Yemen, Rep. 14
Mozambique 14
Moldova 14
Kyrgyz Republic 14
Senegal 14
Bahrain 13
North Macedonia 13
Guatemala 13
Burkina Faso 13
Rwanda 12
Hong Kong SAR, China 11
Botswana 10
Afghanistan 10
Cote d'Ivoire 10
Libya 10
Nicaragua 9
Tajikistan 9
El Salvador 9
Somalia 8
Trinidad and Tobago 8
Angola 7
Namibia 7
Papua New Guinea 7
Madagascar 7
Niger 6
Malta 6
Mauritius 6
Mali 6
Lao PDR 5
Montenegro 5
Honduras 5
Kosovo 5
Jamaica 5
Fiji 5
Gambia, The 5
Benin 5
Korea, Dem. People's Rep. 4
South Sudan 4
Togo 4
Curacao 4
Gabon 4
Brunei Darussalam 4
Greenland 3
Liechtenstein 3
Bermuda 3
Monaco 3
Belize 3
St. Kitts and Nevis 3
Mauritania 3
Sierra Leone 3
Bahamas, The 3
Turkmenistan 3
Liberia 3
French Polynesia 3
Puerto Rico 3
Guinea-Bissau 3
Maldives 3
Seychelles 2
Congo, Rep. 2
Sint Maarten (Dutch part) 2
NA 2
Burundi 2
Guyana 2
Bhutan 2
Haiti 2
Eswatini 2
New Caledonia 2
Macao SAR, China 2
Suriname 2
Chad 2
NA 2
St. Lucia 2
Antigua and Barbuda 2
Andorra 1
Timor-Leste 1
Gibraltar 1
Marshall Islands 1
Micronesia, Fed. Sts. 1
Lesotho 1
St. Vincent and the Grenadines 1
Eritrea 1
NA 1
NA 1
British Virgin Islands 1
Faroe Islands 1
NA 1
Grenada 1
Aruba 1
Guinea 1
Cayman Islands 1
Palau 1
Isle of Man 1
Cabo Verde 1
NA 1
NA 1
NA 1
NA 1
Central African Republic 1
Barbados 1
Sao Tome and Principe 1 
# papers per country
papers_per_country <- works %>% 
  distinct(country_code, id, work_frac, author_position, institution_id) %>% 
  group_by(country_code) %>% 
  summarise(sum_fractional_works = sum(work_frac) %>% round(digits = 1)) %>% 
  arrange(desc(sum_fractional_works)) %>% 
  collect() %>% 
  add_country()
papers_per_country %>% 
  select(country, country_code, sum_fractional_works) %>% 
  knitr::kable()
country country_code sum_fractional_works
United States US 203117.5
Brazil BR 133621.1
China CN 125471.6
United Kingdom GB 52770.8
Spain ES 42338.3
Germany DE 41978.4
India IN 35913.0
Canada CA 33396.2
Japan JP 32637.3
Australia AU 26479.1
Korea, Rep. KR 26068.3
Italy IT 24002.1
Poland PL 22138.4
France FR 21485.3
Indonesia ID 20531.2
South Africa ZA 14615.0
Iran, Islamic Rep. IR 14492.1
Netherlands NL 14466.7
Mexico MX 14207.0
Taiwan, China TW 13904.9
Russian Federation RU 13024.2
Colombia CO 12862.2
Sweden SE 12675.9
Turkiye TR 11371.6
Switzerland CH 10476.5
Malaysia MY 9665.3
Argentina AR 9441.5
Portugal PT 8995.1
Belgium BE 7863.8
Chile CL 7724.4
Norway NO 7441.1
Denmark DK 7122.5
Saudi Arabia SA 6564.3
Czech Republic CZ 5953.0
Israel IL 5819.2
Austria AT 5562.1
Pakistan PK 5545.0
Egypt, Arab Rep. EG 5508.9
Finland FI 4861.7
Ukraine UA 4487.1
Thailand TH 4401.0
Nigeria NG 4234.0
Singapore SG 4192.0
New Zealand NZ 3808.6
Greece GR 3521.3
Ireland IE 3369.2
NA NA 3025.4
Hungary HU 2867.2
Serbia RS 2729.7
Ethiopia ET 2692.8
Croatia HR 2683.6
Romania RO 2662.8
Peru PE 2632.4
Slovenia SI 2283.8
Slovak Republic SK 2183.5
Bangladesh BD 1954.4
Costa Rica CR 1879.4
Ecuador EC 1547.1
Bulgaria BG 1389.9
Lithuania LT 1386.7
Ghana GH 1339.3
Iraq IQ 1281.8
Bahrain BH 1078.6
Morocco MA 1070.8
Nepal NP 1056.8
Uruguay UY 1006.5
United Arab Emirates AE 994.7
Jordan JO 992.7
Vietnam VN 986.5
Estonia EE 985.8
Sri Lanka LK 858.5
Kenya KE 854.9
Philippines PH 775.4
Qatar QA 761.7
Lebanon LB 738.6
Venezuela, RB VE 724.4
Tunisia TN 720.5
Uganda UG 604.0
Mozambique MZ 592.3
Cyprus CY 582.4
Tanzania TZ 549.3
Oman OM 524.7
Kuwait KW 510.6
Cameroon CM 490.3
Latvia LV 441.9
Bosnia and Herzegovina BA 425.8
Cuba CU 403.0
Algeria DZ 359.4
Iceland IS 329.0
Zimbabwe ZW 326.9
El Salvador SV 316.0
Luxembourg LU 303.4
Armenia AM 289.9
Jamaica JM 288.5
Kazakhstan KZ 277.9
Hong Kong SAR, China HK 277.5
West Bank and Gaza PS 276.2
Benin BJ 267.3
Belarus BY 255.3
Sudan SD 254.0
Antigua and Barbuda AG 238.8
Puerto Rico PR 235.8
Paraguay PY 209.9
Cambodia KH 209.4
Syrian Arab Republic SY 197.8
Malta MT 178.1
Botswana BW 177.6
Panama PA 168.5
Zambia ZM 166.0
Bolivia BO 159.1
Uzbekistan UZ 153.0
Montenegro ME 145.9
Lao PDR LA 144.4
Azerbaijan AZ 142.7
Albania AL 141.6
Kosovo XK 141.5
North Macedonia MK 141.4
Guatemala GT 127.2
Malawi MW 124.6
Angola AO 123.5
Yemen, Rep. YE 122.2
Nicaragua NI 118.7
Georgia GE 114.2
Senegal SN 98.7
Mongolia MN 82.4
Brunei Darussalam BN 80.1
Libya LY 74.2
Sao Tome and Principe ST 73.2
Bahamas, The BS 70.7
NA RE 70.6
Namibia NA 68.1
Rwanda RW 65.0
Myanmar MM 63.3
Burkina Faso BF 62.0
Moldova MD 58.6
St. Kitts and Nevis KN 58.2
Dominican Republic DO 57.1
Mauritius MU 56.6
Cote d'Ivoire CI 53.3
Fiji FJ 51.1
Grenada GD 46.4
Mali ML 45.8
NA GP 45.2
Congo, Dem. Rep. CD 44.5
Barbados BB 41.1
Honduras HN 39.6
Tajikistan TJ 32.6
Kyrgyz Republic KG 31.4
Togo TG 31.3
Papua New Guinea PG 28.9
Gabon GA 25.7
Lesotho LS 25.3
Trinidad and Tobago TT 23.6
NA SJ 22.6
Niger NE 21.5
French Polynesia PF 20.3
Afghanistan AF 18.1
Bhutan BT 16.5
Korea, Dem. People's Rep. KP 16.0
Eswatini SZ 15.9
British Virgin Islands VG 15.1
Cabo Verde CV 15.0
Sierra Leone SL 14.7
Bermuda BM 14.5
Congo, Rep. CG 13.6
Gambia, The GM 12.2
Madagascar MG 12.1
Guinea-Bissau GW 12.0
Guinea GN 10.8
Suriname SR 10.3
Curacao CW 9.8
Faroe Islands FO 9.6
Greenland GL 9.1
New Caledonia NC 7.9
Guyana GY 7.9
Burundi BI 6.1
Liechtenstein LI 5.5
St. Vincent and the Grenadines VC 4.2
NA GF 4.2
Marshall Islands MH 4.1
Sint Maarten (Dutch part) SX 3.3
Somalia SO 3.2
Monaco MC 3.1
South Sudan SS 2.9
Turkmenistan TM 2.5
Mauritania MR 2.4
St. Lucia LC 2.3
Chad TD 2.3
Maldives MV 2.0
NA JE 2.0
Liberia LR 1.9
Macao SAR, China MO 1.5
Belize BZ 1.5
Cayman Islands KY 1.0
Micronesia, Fed. Sts. FM 1.0
Andorra AD 0.8
Timor-Leste TL 0.8
Haiti HT 0.8
Aruba AW 0.7
Palau PW 0.6
Gibraltar GI 0.6
Seychelles SC 0.5
Eritrea ER 0.5
NA MQ 0.5
Central African Republic CF 0.4
NA MS 0.3
Isle of Man IM 0.3
NA VA 0.1
NA AX 0.1 
# average apc
average_apc <- works %>%
  # first get rid of duplicates from concepts
  distinct(country_code, id, work_frac, author_position, institution_id,
           APC_in_dollar) %>% 
  group_by(country_code) %>%
  # compute the average APC using fractional authorships as weights
  mutate(sum_frac = sum(work_frac)) %>%
  group_by(country_code, sum_frac) %>%
  summarise(mean_apc = sum(work_frac * APC_in_dollar) / sum_frac) %>% 
  collect() %>% 
  add_country()
## `summarise()` has grouped output by 'country_code'. You can override using the
## `.groups` argument.
# average APC over time
average_apc_time <- works %>%
  # first get rid of duplicates from concepts
  distinct(country_code, id, work_frac, author_position, institution_id,
           APC_in_dollar, publication_year) %>% 
  group_by(country_code, publication_year) %>%
  # compute the average APC using fractional authorships as weights
  mutate(sum_frac = sum(work_frac)) %>%
  group_by(country_code, sum_frac, publication_year) %>%
  summarise(mean_apc = sum(work_frac * APC_in_dollar) / sum_frac) %>% 
  collect() 
## `summarise()` has grouped output by 'country_code', 'sum_frac'. You can override
## using the `.groups` argument.
average_apc_time %>% 
  left_join(wdi, by = c("country_code" = "iso2c")) %>% 
  ggplot(aes(publication_year, mean_apc)) +
  geom_line(aes(group = country), alpha = .3) +
  geom_smooth(se = FALSE, colour = "#007FA8") +
  facet_wrap(vars(region)) +
  scale_x_continuous(breaks = scales::pretty_breaks(6)) +
  coord_cartesian(ylim = c(0, 3000)) +
  labs(x = NULL, y = "Mean APC")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

All three joined

all_three_descriptives <- universities_per_country %>% 
  left_join(papers_per_country, by = c("country", "country_code")) %>% 
  left_join(average_apc, by = c("country", "country_code")) %>% 
  # remove missing values
  # these arise since the wdi data on country codes does not have a mapping for
  # all codes that are present in the data. in most cases, these are small 
  # countries, and in the case of a high university count (433) simply all
  # institutions that were not assigned a country code at all.
  drop_na()

all_three_descriptives %>% 
  arrange(desc(n_universities), desc(sum_fractional_works)) %>% 
  mutate(mean_apc = round(mean_apc, digits = 1),
         sum_fractional_works = scales::comma(sum_fractional_works)) %>% 
  select(Country = country, `n universities` = n_universities,
         `n fractional publications` = sum_fractional_works, 
         `Mean APC` = mean_apc) %>% 
  knitr::kable()
=======
Country n universities n fractional publications Mean APC
United States 6808 203,117.500 1834.2
India 2061 35,913.000 758.8
China 1985 125,471.600 1823.6
United Kingdom 1610 52,770.800 1792.4
Japan 1444 32,637.300 1778.9
Germany 1033 41,978.400 1838.9
France 1024 21,485.300 1596.0
Brazil 807 133,621.100 232.4
Canada 746 33,396.200 1666.3
Russian Federation 665 13,024.200 474.8
Spain 642 42,338.300 804.4
Italy 582 24,002.100 1673.6
Australia 538 26,479.100 1804.4
Korea, Rep. 478 26,068.300 1676.7
Indonesia 407 20,531.200 153.7
Poland 333 22,138.400 779.0
Netherlands 329 14,466.700 1851.9
Switzerland 293 10,476.500 1912.1
Taiwan, China 264 13,904.900 1800.1
Turkiye 240 11,371.600 781.4
Pakistan 240 5,545.000 1083.2
Mexico 236 14,207.000 582.3
Iran, Islamic Rep. 234 14,492.100 721.1
Norway 219 7,441.100 1597.1
Colombia 205 12,862.200 198.6
Ukraine 199 4,487.100 290.1
Czech Republic 195 5,953.000 1054.1
Sweden 186 12,675.900 1786.4
Portugal 181 8,995.100 784.8
Austria 177 5,562.100 1677.0
Nigeria 171 4,234.000 824.5
South Africa 168 14,615.000 978.7
Belgium 158 7,863.800 1676.6
Thailand 156 4,401.000 1397.1
Argentina 151 9,441.500 398.2
Greece 145 3,521.300 1446.4
Finland 144 4,861.700 1598.4
Denmark 142 7,122.500 1778.9
Ireland 138 3,369.200 1738.8
Bangladesh 132 1,954.400 766.2
Philippines 122 775.400 931.1
Malaysia 121 9,665.300 1053.4
Israel 120 5,819.200 1917.2
Hungary 112 2,867.200 1250.0
New Zealand 108 3,808.600 1579.4
Vietnam 103 986.500 1254.8
Chile 97 7,724.400 521.6
Singapore 97 4,192.000 1874.8
Egypt, Arab Rep. 96 5,508.900 912.7
Saudi Arabia 91 6,564.300 1308.2
Slovak Republic 89 2,183.500 628.9
Kenya 86 854.900 1514.6
Romania 80 2,662.800 833.5
Bulgaria 80 1,389.900 513.7
Peru 78 2,632.400 155.5
Slovenia 76 2,283.800 961.9
Ecuador 65 1,547.100 261.7
Uganda 64 604.000 1754.4
Serbia 60 2,729.700 538.9
United Arab Emirates 60 994.700 1404.5
Croatia 59 2,683.600 437.4
Ethiopia 54 2,692.800 1576.0
Ghana 54 1,339.300 1375.4
Iraq 52 1,281.800 658.6
Kazakhstan 51 277.900 968.9
Tanzania 48 549.300 1648.7
Algeria 47 359.400 505.7
Nepal 46 1,056.800 739.2
Belarus 40 255.300 509.3
Venezuela, RB 39 724.400 240.7
Sri Lanka 38 858.500 1159.4
Latvia 38 441.900 508.6
Cuba 38 403.000 386.5
Lithuania 36 1,386.700 819.0
Estonia 33 985.800 893.3
Tunisia 33 720.500 1068.2
Jordan 32 992.700 1195.4
Morocco 28 1,070.800 1562.6
Zimbabwe 28 326.900 1082.5
Uruguay 27 1,006.500 413.3
Cameroon 27 490.300 1434.3
Sudan 27 254.000 1272.5
Uzbekistan 27 153.000 488.3
Lebanon 26 738.600 1449.2
Cyprus 26 582.400 1292.7
Costa Rica 25 1,879.400 106.2
Luxembourg 23 303.400 1740.8
Armenia 23 289.900 1349.2China 460832.3
United States 456678.0
Brazil 266241.3
United Kingdom 130906.7
Germany 114970.8
Japan 90599.2
Canada 87827.8
Spain 84138.8
South Korea 79297.7
Australia 78220.5
Italy 77622.7
Netherlands 42592.2
Taiwan 40111.3
Sweden 38855.0
Poland 37813.5
France 31836.3
Switzerland 29575.2
Iran 28694.7
India 27611.5
South Africa 25886.0
Belgium 24577.5
Denmark 19803.7
Malaysia 18720.5
Turkey 18233.7
Mexico 17757.8
Austria 17341.0
Portugal 16621.8
Israel 15475.0
Norway 14952.8
Saudi Arabia 12935.7
Finland 12476.2
Russia 11144.3
Singapore 11055.0
Egypt 10171.3
Thailand 10076.0
Greece 9671.2
New Zealand 9474.0
Colombia 9076.3
Czech Republic 8389.5
Chile 8320.2
Ireland 7870.3
Argentina 7579.8
Hungary 6309.3
Serbia 6008.7
Pakistan 4285.5
Slovenia 4175.7
Croatia 3819.3
Nigeria 2140.8
Romania 1835.3
Uruguay 1702.5
Slovakia 1628.2
Estonia 1590.0
Uganda 1561.7
Jordan 1508.5
United Arab Emirates 1500.0
Lebanon 1475.3
Oman 1250.8
Lithuania 1221.0
Tunisia 1221.0
Ghana 1194.2
Ethiopia 1106.8
Kuwait 876.5
Iceland 778.3
Qatar 650.8
Luxembourg 643.8
Morocco 538.3
Viet Nam 463.5
Cyprus 383.8
Algeria 117.8 
# average apc
average_apc <- works %>%
  # first get rid of duplicates from concepts
  distinct(country, country_code, id, work_frac, author_position, institution_id,
           APC_in_dollar) %>% 
  group_by(country) %>%
  # compute the average APC using fractional authorships as weights
  mutate(sum_frac = sum(work_frac)) %>%
  group_by(country, sum_frac) %>%
  summarise(mean_apc = sum(work_frac * APC_in_dollar) / sum_frac) %>% 
  collect()
## `summarise()` has grouped output by 'country'. You can override using the
## `.groups` argument.
# average APC over time
average_apc_time <- works %>%
  # first get rid of duplicates from concepts
  distinct(country, country_code, id, work_frac, author_position, institution_id,
           APC_in_dollar, publication_year) %>% 
  group_by(country, country_code, publication_year) %>%
  # compute the average APC using fractional authorships as weights
  mutate(sum_frac = sum(work_frac)) %>%
  group_by(country, country_code, sum_frac, publication_year) %>%
  summarise(mean_apc = sum(work_frac * APC_in_dollar) / sum_frac) %>% 
  collect()
## `summarise()` has grouped output by 'country', 'country_code', 'sum_frac'. You
## can override using the `.groups` argument.
average_apc_time %>% 
  left_join(wdi, by = c("country_code" = "iso2c")) %>% 
  ggplot(aes(publication_year, mean_apc)) +
  geom_line(aes(group = country), alpha = .3) +
  geom_smooth(se = FALSE, colour = "#007FA8") +
  facet_wrap(vars(region)) +
  scale_x_continuous(breaks = scales::pretty_breaks(6)) +
  coord_cartesian(ylim = c(0, 3000)) +
  labs(x = NULL, y = "Mean APC")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

All three joined

all_three_descriptives <- universities_per_country %>% 
  left_join(papers_per_country, by = "country") %>% 
  left_join(average_apc, by = "country")

all_three_descriptives %>% 
  arrange(desc(n_universities), desc(sum_fractional_works)) %>% 
  mutate(mean_apc = round(mean_apc, digits = 1),
         sum_fractional_works = scales::comma(sum_fractional_works)) %>% 
  select(Country = country, `n universities` = n_universities,
         `n fractional publications` = sum_fractional_works, 
         `Mean APC` = mean_apc) %>% 
  knitr::kable()
>>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= >>>>>>> main
Country n universities n fractional publications Mean APC
Georgia 22 114.200 853.7
Oman 21 524.700 528.2
Cambodia 20 209.400 975.8
Bolivia 20 159.100 166.9
Albania 20 141.600 434.2
Qatar 19 761.700 1327.9
Kuwait 19 510.600 1467.0
Bosnia and Herzegovina 19 425.800 340.9
Iceland 19 329.000 1345.1
Syrian Arab Republic 19 197.800 831.1
Azerbaijan 19 142.700 620.6
Myanmar 19 63.300 522.0
Congo, Dem. Rep. 18 44.500 1426.9
West Bank and Gaza 17 276.200 1419.5
Paraguay 17 209.900 79.2
Zambia 16 166.000 1527.3
Panama 15 168.500 1354.7
Malawi 15 124.600 1749.7
Dominican Republic 15 57.100 439.5
Mozambique 14 592.300 257.5
Yemen, Rep. 14 122.200 1044.3
Senegal 14 98.700 942.6
Mongolia 14 82.400 554.6
Moldova 14 58.600 657.7
Kyrgyz Republic 14 31.400 1126.3
Bahrain 13 1,078.600 1610.1
North Macedonia 13 141.400 447.3
Guatemala 13 127.200 327.1
Burkina Faso 13 62.000 1704.4
Rwanda 12 65.000 1537.9
Hong Kong SAR, China 11 277.500 1767.6
Botswana 10 177.600 845.7
Libya 10 74.200 855.0
Cote d'Ivoire 10 53.300 888.9China 216 460,832.3 2002.6
United States 199 456,678.0 2099.0
United Kingdom 60 130,906.7 2042.1
Japan 55 90,599.2 1920.8
Germany 54 114,970.8 2014.4
South Korea 46 79,297.7 1843.7
Spain 42 84,138.8 1176.2
Italy 41 77,622.7 1919.2
India 38 27,611.5 1052.3
Iran 36 28,694.7 818.1
Australia 32 78,220.5 1982.5
Brazil 31 266,241.3 429.0
Poland 31 37,813.5 1149.0
Turkey 31 18,233.7 933.4
Canada 30 87,827.8 1926.1
France 28 31,836.3 1963.1
Taiwan 21 40,111.3 1920.0
Netherlands 13 42,592.2 2055.0
Sweden 12 38,855.0 2033.2
Austria 12 17,341.0 1958.8
Afghanistan 1018.100 891.311,144.3 734.9
El Salvador 9316.000 27.4
Nicaragua 9 118.700 117.4
Tajikistan 9 32.600 180.425,886.0 1167.1
Switzerland 8 29,575.2 2190.5
Belgium 8 24,577.5 1955.2
Trinidad and Tobago 823.600 892.815,475.0 2189.4
Somalia 83.200 922.9
Angola 7 123.500 136.610,171.3 991.9
Greece 8 9,671.2 1643.8
Namibia 768.100 1082.112,476.2 1899.1
Papua New Guinea 728.900 1175.29,474.0 1861.0
Madagascar 712.100 1135.1
Malta 6 178.100 1060.9
Mauritius 6 56.600 948.3
Mali 6 45.800 1861.38,389.5 1371.1
Malaysia 6 18,720.5 1271.4
Mexico 6 17,757.8 1072.4
Portugal 6 16,621.8 1231.3
Niger 621.500 1341.9
Jamaica 5 288.500 1294.6
Benin 5 267.300 1542.1
Montenegro 5 145.900 443.614,952.8 1877.1
Thailand 6 10,076.0 1673.7
Ireland 6 7,870.3 1998.1
Hungary 6 6,309.3 1791.9
Lao PDR 5144.400 1535.219,803.7 2008.1
Kosovo 5141.500 657.212,935.7 1561.1
Fiji 551.100 1053.1
Honduras 5 39.600 552.8
Gambia, The 5 12.200 1330.9
Brunei Darussalam 4 80.100 866.3
Togo 4 31.300 1168.4
Gabon 4 25.700 2010.2
Korea, Dem. People's Rep. 4 16.000 522.6
Curacao 4 9.800 1305.8
South Sudan 4 2.900 1242.2
Puerto Rico 3 235.800 1745.7
Bahamas, The 3 70.700 68.24,285.5 1190.0
Singapore 3 11,055.0 2198.9
Colombia 3 9,076.3 445.3
St. Kitts and Nevis 358.200 1867.0
French Polynesia 3 20.300 1685.98,320.2 1075.9
Argentina 3 7,579.8 664.7
Sierra Leone 314.700 1733.46,008.7 765.2
Bermuda 314.500 1786.11,835.3 1129.6
Guinea-Bissau 312.000 2115.51,221.0 1378.1
Greenland 3 9.100 925.7
Liechtenstein 3 5.500 1038.0
Monaco 3 3.100 559.7
Turkmenistan 3 2.500 583.0
Mauritania 3 2.400 2160.2
Maldives 3 2.000 561.7
Liberia 3 1.900 1894.5
Belize 3 1.500 493.6
Antigua and Barbuda 2238.800 782.54,175.7 1133.1
Bhutan 216.500 284.9
Eswatini 2 15.900 1097.1
Congo, Rep. 2 13.600 1481.12,140.8 1251.3
Slovakia 2 1,628.2 918.8
Jordan 2 1,508.5 1444.6
Suriname 210.300 1664.01,500.0 1876.0
Guyana 2 7.900 841.2
New Caledonia 2 7.900 1129.7
Burundi 2 6.100 899.1
Sint Maarten (Dutch part) 2 3.300 716.5
Chad 2 2.300 1534.5
St. Lucia 2 2.300 1605.0
Haiti 2 0.800 1184.9
Seychelles 2 0.500 1607.5
Sao Tome and Principe 173.200 238.43,819.3 607.2
Grenada 146.400 1274.21,702.5 690.7
Barbados 141.100 1034.9
Lesotho 1 25.300 574.5
British Virgin Islands 1 15.100 1298.5
Cabo Verde 1 15.000 221.9
Guinea 1 10.800 1612.11,590.0 1588.3
Uganda 1 1,561.7 1895.4
Lebanon 1 1,475.3 1832.5
Oman 1 1,250.8 618.6
Lithuania 1 1,221.0 1351.3
Faroe Islands 19.600 1128.0
St. Vincent and the Grenadines 1 4.200 1091.31,194.2 1721.3
Ethiopia 1 1,106.8 1750.5
Marshall Islands 14.100 448.7876.5 1673.2
Micronesia, Fed. Sts. 11.000 1288.9778.3 1698.7
Cayman Islands 11.000 422.1650.8 1742.2
Andorra 10.800 1800.6
Timor-Leste 1 0.800 830.0
Aruba 1 0.700 1493.4
Gibraltar 1 0.600 1149.9643.8 2149.2
Morocco 1 538.3 980.9
Viet Nam 1 463.5 1518.1
Cyprus 1 383.8 1760.4
Palau 1 0.600 1770.1
Eritrea 1 0.500 258.3
Central African Republic 1 0.400 1483.0
Isle of Man 10.300 2235.4117.8 838.8
gdp <- WDI::WDI(start = 2019, end = 2019)

# plot n papers against average apc
p <- all_three_descriptives %>% 
  left_join(wdi, by = c("country_code" = "iso2c", "country" = "country")) %>% 
  ggplot(aes(sum_fractional_works, mean_apc, colour = region, label = country)) +
  geom_point() +
  scale_x_log10(labels = scales::comma) +
  scale_y_continuous(labels = scales::comma) +
  theme(legend.position = "top") +
  labs(y = NULL, colour = NULL, x = "Sum of fractional publications")
p
<<<<<<< HEAD 

plotly::ggplotly(p)
======= 

plotly::ggplotly(p)
>>>>>>> main 
pdata <- all_three_descriptives %>% 
  left_join(gdp, by = c("country_code" = "iso2c", "country" = "country")) %>% 
  left_join(wdi, by = c("country_code" = "iso2c", "country" = "country"))

labels <- pdata %>% 
  mutate(label = case_when(
    country %in% c("China", "India", "United States","Uganda",
                      "Brazil", "Switzerland", "Israel", "Spain",
                     "Saudi Arabia") ~ country,
    TRUE ~ ""))

p <- pdata %>% 
  ggplot(aes(NY.GDP.PCAP.KD, mean_apc, colour = region, label = country)) +
  geom_point(aes(alpha = sum_fractional_works)) +
  ggrepel::geom_text_repel(data = labels, aes(label = label),
                           show.legend = FALSE, max.overlaps = Inf,
                           box.padding = 1, min.segment.length = 0,
                           nudge_y = -10) +
  scale_x_continuous(labels = scales::dollar) +
  scale_y_continuous(labels = scales::dollar) +
  scale_alpha_continuous(trans = "log10", range = c(.1, 1),
                         labels = scales::comma) +
  scale_colour_discrete_qualitative(palette = "Dark 3") +
  theme(legend.position = "top", legend.box = "vertical") +
  labs(y = "Mean APC", colour = NULL, x = "GDP per capita", 
       alpha = "Number of fractional publications")
p
<<<<<<< HEAD 

plotly::ggplotly(p)
======= 

plotly::ggplotly(p)
>>>>>>> main

Across topics within continents

papers_per_country_per_field <- works %>% 
  mutate(total_weight = work_frac * concept_frac) %>% 
  distinct(country, country_code, id, author_position, institution_id, field,
           total_weight) %>% 
  group_by(country, country_code, field) %>% 
  summarise(sum_fractional_works = sum(total_weight) %>% round(digits = 1)) %>% 
  arrange(desc(sum_fractional_works)) %>% 
  collect()
## `summarise()` has grouped output by 'country', 'country_code'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'country', 'country_code'. You can override using the `.groups` argument.
plot_data <- papers_per_country_per_field %>% 
  left_join(wdi, by = c("country_code" = "iso2c")) %>% 
  group_by(region, field) %>% 
  summarise(sum_fractional_works = sum(sum_fractional_works)) %>% 
  mutate(prop = sum_fractional_works / sum(sum_fractional_works),
<<<<<<< HEAD
         label = case_when(
           prop > .15 ~ scales::percent(prop, accuracy = .1),
           TRUE ~ "")
  )
## `summarise()` has grouped output by 'region'. You can override using the
## `.groups` argument.
plot_data %>% 
=======
         label = glue::glue(
           "{scales::comma(sum_fractional_works)} ({scales::percent(prop, accuracy = .1)})"
         )
  )

plot_data %>% 
  ggplot(aes(sum_fractional_works, fct_reorder(region, prop))) +
  geom_segment(aes(xend = 0, yend = region), colour = "grey70") +
  geom_point() +
  geom_text(aes(label = label), nudge_x = 2000, hjust = "left") +
  scale_x_continuous(expand = expansion(mult = c(0.05, .25)),
                     labels = scales::comma) +
  labs(x = "Number of fractional publications", y = NULL) +
  theme(panel.grid = element_blank(),
        panel.grid.major.x = element_line(colour = "grey92"),
        axis.text.y = element_text(margin = margin(r = -10)))

Distribution across topics

How many papers do we have, which also have a topic?

works %>% 
  distinct(id) %>% 
  sdf_nrow()
## [1] 1572417

This is our total sample size.

Which topics are represented in our sample?

frac_concept_papers <- works %>% 
  distinct(id, field, concept_frac) %>% 
  group_by(field) %>% 
  summarise(frac_papers = sum(concept_frac)) %>% 
  arrange(desc(frac_papers)) %>% 
  collect()
plot_data <- frac_concept_papers %>% 
>>>>>>> main
  drop_na() %>% 
  ggplot(aes(prop, fct_reorder(field, prop))) +
  geom_segment(aes(xend = 0, yend = field), colour = "grey70") +
  geom_point() +
  geom_text(aes(label = label), nudge_x = .015, hjust = "left") +
  facet_wrap(vars(region)) +
  scale_x_continuous(expand = expansion(mult = c(0.05, .25)),
                     labels = function(x) scales::percent(x, accuracy = 1)) +
  labs(x = "% of fractional publications", y = NULL) +
  theme(panel.grid = element_blank(),
        panel.grid.major.x = element_line(colour = "grey92"),
<<<<<<< HEAD
        axis.text.y = element_text(margin = margin(r = -2)))

======= axis.text.y = element_text(margin = margin(r = -10)))

What is the average apc across topics?

apc_field <- works %>% 
  distinct(id, field, concept_frac, APC_in_dollar) %>% 
  group_by(field) %>%
  mutate(sum_frac = sum(concept_frac)) %>%
  group_by(field, sum_frac) %>%
  summarise(mean_apc = sum(concept_frac * APC_in_dollar) / sum_frac) %>% 
  collect()
## `summarise()` has grouped output by 'field'. You can override using the
## `.groups` argument.
plot_data %>% 
  mutate(frac_papers = scales::comma(frac_papers),
         prop = scales::percent(prop, accuracy = .1)) %>% 
  knitr::kable()
field frac_papers prop label
Medicine 481,047 30.6% 481,047 (30.6%)
Biology 290,499 18.5% 290,499 (18.5%)
Chemistry 163,767 10.4% 163,767 (10.4%)
Computer science 140,438 8.9% 140,438 (8.9%)
Materials science 106,550 6.8% 106,550 (6.8%)
Psychology 98,461 6.3% 98,461 (6.3%)
Environmental science 46,768 3.0% 46,768 (3.0%)
Physics 44,382 2.8% 44,382 (2.8%)
Political science 36,339 2.3% 36,339 (2.3%)
Geography 31,787 2.0% 31,787 (2.0%)
Sociology 29,340 1.9% 29,340 (1.9%)
Mathematics 24,093 1.5% 24,093 (1.5%)
Art 21,573 1.4% 21,573 (1.4%)
Business 20,992 1.3% 20,992 (1.3%)
Geology 13,937 0.9% 13,937 (0.9%)
Philosophy 10,596 0.7% 10,596 (0.7%)
Economics 5,709 0.4% 5,709 (0.4%)
History 3,379 0.2% 3,379 (0.2%)
Engineering 2,760 0.2% 2,760 (0.2%) 
p_apc_field <- apc_field %>% 
  drop_na() %>% 
  ggplot(aes(mean_apc, fct_reorder(field, mean_apc))) +
  geom_segment(aes(xend = 0, yend = field), colour = "grey70") +
  geom_point() + 
  geom_text(aes(label = scales::comma(mean_apc, accuracy = 1)), nudge_x = 30, 
            hjust = "left") +
  labs(y = NULL, x = "Mean APC (in $)") +
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.ticks = element_blank(),
        panel.border = element_blank()) +
  scale_x_continuous(expand = expansion(mult = c(0, .1)),
                     labels = scales::comma)
p_apc_field

Association between P_top10 and APC

get_mean_apc_by_author_position <- function(df) {
  df %>%
    # first get rid of duplicates from concepts
    distinct(id, author_position, work_frac, APC_in_dollar, University, country,
             publication_year, P_top10) %>% 
    group_by(University, publication_year, country, P_top10) %>%
    # compute the average APC using fractional authorships as weights
    mutate(sum_frac = sum(work_frac)) %>%
    group_by(University, publication_year, country, P_top10, sum_frac,
             author_position) %>%
    summarise(mean_apc = sum(work_frac * APC_in_dollar) / sum_frac,
              fractional_works = sum(work_frac))
}

mean_apcs <- works %>% 
  filter(publication_year == last_year_of_period) %>% 
  get_mean_apc_by_author_position()

mean_apcs_local <- mean_apcs %>%
  collect()
## `summarise()` has grouped output by 'University', 'publication_year', 'country',
## 'P_top10', 'sum_frac'. You can override using the `.groups` argument.
mean_apc_16_19 <- works %>% 
  filter(first_year_of_period == 2016) %>% 
  get_mean_apc_by_author_position()

mean_apc_16_19_local <- mean_apc_16_19 %>% 
  collect()
## `summarise()` has grouped output by 'University', 'publication_year', 'country',
## 'P_top10', 'sum_frac'. You can override using the `.groups` argument.
# plot for 2016-19

# taking out the correlation, because they are incorrect given that the figure
# shows a non-linear relationship (x-axis logged), but the correlation is linear
# (and quite unsuitable to the skewed P_top10)
p1 <- mean_apc_16_19_local %>%
  mutate(author_position = recode(author_position, first = "First authors", 
                                  last = "Last authors")) %>% 
  ggplot(aes(P_top10, mean_apc, colour = fractional_works)) + 
  geom_point(aes(), alpha = .5) +
  scale_colour_continuous_sequential(palette = "Mako", trans = "log10",
                                     labels = comma) +
  geom_smooth(colour = "grey30") +
  facet_wrap(vars(author_position)) +
  scale_x_log10() +
  scale_y_continuous(labels = dollar) +
  labs(caption = "Fractional counting; 2016-2019", y = "Mean APC",
       colour = "Number of papers per institution",
       x = expression(P["top 10%"])) +
  theme(legend.position = "top",
        legend.key.width = unit(1.5, 'cm'))
p1
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

p2 <- mean_apcs_local %>%
  mutate(author_position = recode(author_position, first = "First authors", 
                                  last = "Last authors")) %>% 
  group_by(publication_year, author_position) %>%
  mutate(ptop10_quantiles = cut_quartiles(P_top10)) %>%
  group_by(ptop10_quantiles, publication_year, author_position) %>%
  summarise(mean_apc = weighted.mean(mean_apc, sum_frac, na.rm = TRUE), 
            .groups = "drop_last") %>%
  ggplot(aes(publication_year, mean_apc, colour = ptop10_quantiles,
             group = ptop10_quantiles)) +
  geom_line() +
  facet_wrap(vars(author_position)) +
  scale_x_continuous(breaks = seq(2010, 2018, by = 4)) +
  scale_y_continuous(labels = dollar) +
  scale_color_discrete_qualitative(palette = "Dark 3") +
  theme(legend.position = "top") +
  labs(caption = "Fractional counting", y = "Mean APC",
       colour = expression(P["top 10%"]), x = NULL)
p2

p1 / p2 +
  plot_layout(heights = c(4.5, 4)) +
  plot_annotation(tag_levels = "A")
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

get_mean_apc_by_concept <- function(df) {
  df %>%
    distinct(id, University, publication_year, P_top10, field, work_frac, 
             APC_in_dollar, author_position) %>% 
    group_by(University, publication_year, P_top10, field) %>%
    # spark is unhappy for some reason, so coerce again to numeric
    mutate(work_frac = as.numeric(work_frac)) %>% 
    # compute the average APC using fractional authorships as weights
    mutate(sum_frac = sum(work_frac)) %>%
    group_by(University, publication_year, P_top10, sum_frac,
             author_position, field) %>%
    summarise(mean_apc = sum(work_frac * APC_in_dollar) / sum_frac)
}

mean_apc_concept <- works %>% 
  filter(publication_year == last_year_of_period) %>% 
  get_mean_apc_by_concept()

mean_apc_concept_local <- mean_apc_concept %>%
  collect()
## `summarise()` has grouped output by 'University', 'publication_year', 'P_top10',
## 'sum_frac', 'author_position'. You can override using the `.groups` argument.
mean_apc_concept_16_19 <- works %>% 
  filter(first_year_of_period == 2016) %>% 
  get_mean_apc_by_concept()

mean_apc_concept_16_19_local <- mean_apc_concept_16_19 %>% 
  collect()
## `summarise()` has grouped output by 'University', 'publication_year', 'P_top10',
## 'sum_frac', 'author_position'. You can override using the `.groups` argument.
mean_apc_concept_16_19_local <- mean_apc_concept_16_19_local %>% 
  mutate(author_position = recode(author_position, first = "First authors", 
                                  last = "Last authors"))
# plot for 2016-2019
p <- mean_apc_concept_16_19_local %>%
  drop_na(field) %>% 
  ggplot(aes(P_top10, mean_apc, colour = field)) +
  geom_smooth(alpha = .15) +
  facet_wrap(vars(author_position), nrow = 1) +
  scale_x_log10() +
  scale_y_continuous(labels = dollar) + 
  labs(caption = "2016-2019", y = "Mean APC",
       x = expression(P["top 10%"]))
p
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

plotly::ggplotly(p)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Using ggrepel

field_selection <- mean_apc_concept_16_19_local %>%
  drop_na(field) %>% 
  filter(field %in% c("Medicine", "History", "Sociology", "Biology",
                      "Materials science", "Physics"))
  
final_ptop_apc_field <- mean_apc_concept_16_19_local %>% 
  anti_join(field_selection) %>% 
  ggplot(aes(P_top10, mean_apc, group = field)) +
  geom_smooth(alpha = .3, colour = "grey80", fill = "grey90") +
  geom_smooth(aes(colour = field), data = field_selection) +
  facet_wrap(vars(author_position), nrow = 1) +
  scale_x_log10() +
  scale_y_continuous(labels = dollar) + 
  scale_color_discrete_qualitative(palette = "Dark 3") +
  labs(caption = "Time period: 2016-2019", y = "Mean APC", colour = NULL,
       x = expression(P["top 10%"])) +
  theme(legend.position = "top") +
  guides(colour = guide_legend(override.aes = list(alpha = 0),
                               nrow = 1))
## Joining, by = c("University", "publication_year", "P_top10", "sum_frac",
## "author_position", "field", "mean_apc")
final_ptop_apc_field
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

(final_ptop_apc_field + 
   # https://stackoverflow.com/a/65946462/3149349
   theme(axis.title.y = element_text(margin = margin(r = -120, unit = "pt")))
   ) / p_apc_field +
  plot_layout(heights = c(3.5, 5)) +
  plot_annotation(tag_levels = "A")
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Over time - first-authors

mean_apc_concept_local %>%
  filter(!is.na(field), author_position == "first") %>% 
  group_by(publication_year, field) %>%
  mutate(ptop10_quantiles = cut_quartiles(P_top10)) %>%
  group_by(ptop10_quantiles, publication_year, field) %>%
  summarise(mean_apc = weighted.mean(mean_apc, sum_frac, na.rm = TRUE), 
            .groups = "drop_last") %>%
  ggplot(aes(publication_year, mean_apc, colour = ptop10_quantiles,
             group = ptop10_quantiles)) +
  geom_line() +
  facet_wrap(vars(field)) +
  scale_x_continuous(breaks = seq(2010, 2018, by = 4)) +
  scale_color_discrete_qualitative(palette = "Dark 3") +
  theme(legend.position = "top") +
  labs(caption = "Fractional counting", y = "Mean APC",
       colour = expression(P["top 10%"]), x = NULL)

Over time - last-authors

mean_apc_concept_local %>%
  filter(!is.na(field), author_position == "last") %>% 
  group_by(publication_year, field) %>%
  mutate(ptop10_quantiles = cut_quartiles(P_top10)) %>%
  group_by(ptop10_quantiles, publication_year, field) %>%
  summarise(mean_apc = weighted.mean(mean_apc, sum_frac, na.rm = TRUE), 
            .groups = "drop_last") %>%
  ggplot(aes(publication_year, mean_apc, colour = ptop10_quantiles,
             group = ptop10_quantiles)) +
  geom_line() +
  facet_wrap(vars(field)) +
  scale_x_continuous(breaks = seq(2010, 2018, by = 4)) +
  scale_color_discrete_qualitative(palette = "Dark 3") +
  theme(legend.position = "top") +
  labs(caption = "Fractional counting", y = "Mean APC",
       colour = expression(P["top 10%"]), x = NULL)

Country comparison

mean_apc_country_16_19 <- works %>%
  filter(first_year_of_period == 2016) %>% 
  # first get rid of duplicates from concepts
  distinct(id, work_frac, APC_in_dollar, University, country, P_top10, country_code) %>% 
  # spark is unhappy for some reason, so coerce again to numeric
  mutate(work_frac = as.numeric(work_frac)) %>% 
  group_by(University, country, P_top10) %>%
  # compute the average APC using fractional authorships as weights
  mutate(sum_frac = sum(work_frac)) %>%
  group_by(University, country, P_top10, sum_frac, country_code) %>%
  summarise(mean_apc = sum(work_frac * APC_in_dollar) / sum_frac)

mean_apc_country_16_19_local <- mean_apc_country_16_19 %>%
  collect()
## `summarise()` has grouped output by 'University', 'country', 'P_top10',
## 'sum_frac'. You can override using the `.groups` argument.
mean_apc_country_16_19_local <- mean_apc_country_16_19_local %>% 
  left_join(wdi, by = c("country_code" = "iso2c"))
mean_apc_country_16_19_local %>% 
  ggplot(aes(P_top10, mean_apc, colour = region)) +
  geom_point(alpha = .3, size = 1.2) +
  geom_smooth(alpha = .3) +
  scale_x_log10() +
  scale_y_continuous(labels = dollar) +
  scale_color_discrete_qualitative(palette = "Dark 3") +
  labs(y = "Mean APC", x = expression(P["top 10%"]), colour = NULL) +
  theme(legend.position = "top") +
  guides(colour = guide_legend(override.aes = list(alpha = 0)))
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

>>>>>>> main 
spark_disconnect(sc)